Make AI recommend your business.
Find out why AI isn't recommending you — and exactly what to fix.
Add to Chrome — 1-click analysis on any siteStart your audit for free
How it works
From “Invisible” to “Recommended” in 3 steps.
The AI Audit
We query ChatGPT, Gemini, and Perplexity to see how they describe your business — and your competitors. Real answers from real AI.
The Visibility Score
Get a clear grade on your Citation Share. See exactly how often you are recommended vs. the competition — in one number you can act on.
The Action Plan
A plain-English checklist of 3–5 changes to your website that make AI recognise you as an authority — and recommend you over everyone else.
Everything you need to win AI search
LLM Readiness Score
Instant score out of 100 showing exactly how AI-ready your site is today.
Structured Data Analysis
Schema markup, metadata, and technical signals AI models use to understand your content.
Content Clarity Check
Measures how clearly your content communicates to large language models like ChatGPT and Gemini.
Actionable Recommendations
Prioritised fixes ranked by impact so you know exactly what to improve first.
Score HistoryPremium
Track your improvements over time and see how your score changes with every update.
AI Visibility Check
See if your website is recommended by ChatGPT, Gemini, and Perplexity when users search for your products or services.
The AI search era is already here
Your competitors are already showing up in AI results. Are you?
ChatGPT, Gemini, and Perplexity are now the first stop for millions of buying decisions. Businesses that optimise for AI visibility today will dominate their category tomorrow — those that don't will become invisible.
60%+
of online searches now involve an AI assistant at some point in the journey
#1 position
in AI results drives more trust than any traditional search ranking
Most sites
fail basic AI readiness checks — a huge opportunity for those who act now
Know where you stand. Fix what matters. Get ahead — and stay there.
See it in action
What your report reveals
In seconds, you get a full picture of how AI models see your website — and exactly what to fix.
See your AI visibility score
A single number—0 to 100—showing exactly how visible you are to AI right now.
Prioritized fixes, not a wall of text
Every recommendation ranked by impact so you know exactly what to tackle first.
How each AI sees your brand
Check your citation status across ChatGPT, Gemini, and Perplexity in one view.
Track your progress over time
Watch your AI visibility score climb week by week as you implement fixes.
Methodology
Traditional SEO vs. Answer Engine Optimization (AEO)
AI assistants don't rank pages — they cite sources. AEO is the discipline of becoming that source.
| Feature | Traditional SEO | LLM-Ready (AEO) |
|---|---|---|
| Primary Goal | Ranking for Keywords | Citation in Generative Answers |
| Indexing | Crawler-based (Googlebot) | Training Data + RAG Retrieval |
| Content Focus | Search Volume / Traffic | Semantic Density / Information Gain |
| Primary Metric | Position 1–10 | Weighted Visibility Score (WVS) |
| User Intent | Discovery via Browsing | Direct Recommendation via Logic |
Knowledge Base
AEO Technical Glossary
Precise definitions for AI search optimization concepts.
What is Answer Engine Optimization (AEO)?
Answer Engine Optimization (AEO) is the technical process of structuring website content to be accurately retrieved, synthesized, and cited by Large Language Models (LLMs) like ChatGPT, Gemini, and Perplexity. Unlike traditional SEO, which focuses on click-through rates from blue links, AEO focuses on Citation Velocity and Entity Authority within generative responses.
What is the Weighted Visibility Score (WVS)?
The Weighted Visibility Score is a proprietary metric developed by LLM Check to quantify a brand's "Share of Voice" in AI search. It calculates visibility by weighting four distinct factors:
- Mention Rate: The frequency of brand appearance across 5 intent-based prompts.
- Prominence: The positional rank of the brand within an LLM's recommendation list.
- Sentiment Alignment: The accuracy of the LLM's description of the brand's core features.
- Provenance: The presence of a direct, clickable source link back to the brand's domain.
What are common AI visibility blockers?
- Semantic Drift — When a brand's marketing language uses non-standard jargon that differs from the LLM's training data for that category, causing misalignment between how the brand describes itself and how AI models understand it.
- Citation Walls — Technical barriers such as aggressive WAF settings or JavaScript-only rendering that prevent AI retrieval crawlers (OAI-SearchBot, PerplexityBot) from verifying site claims.
- Information Thinness — Content that lacks unique data points, proprietary statistics, or expert attributions, causing LLMs to prefer more data-dense competitors as primary sources.
Methodology
The LLM-Ready Protocol
The technical checklist LLM Check uses to establish whether a site is citable by AI assistants.
Atomic Answers
A 40–60 word direct answer placed immediately after every H2 question header, giving AI retrieval systems a clean, extractable response without ambiguity.
Entity Mapping
Explicitly linking your brand to known industry entities (e.g. SaaS, SEO, AI) so LLMs can place you in the correct knowledge category during retrieval.
Freshness Signal
Maintaining a dateModified property in JSON-LD schema to signal temporal relevance to AI crawlers that prioritize recently updated primary sources.
Markdown Parity
Ensuring site content is fully readable when CSS and JavaScript are stripped — the format in which text-only AI agents and RAG pipelines ingest web content.